python处理xml文件

2023-08-19 14:19:40

参考：https://docs.python.org/2/library/xml.etree.elementtree.html

例子：

<?xml version="1.0"?>

<data>

    <country name="Liechtenstein">

        <rank>1</rank>

        <year>2008</year>

        <gdppc>141100</gdppc>

        <neighbor name="Austria" direction="E"/>

        <neighbor name="Switzerland" direction="W"/>

    </country>

    <country name="Singapore">

        <rank>4</rank>

        <year>2011</year>

        <gdppc>59900</gdppc>

        <neighbor name="Malaysia" direction="N"/>

    </country>

    <country name="Panama">

        <rank>68</rank>

        <year>2011</year>

        <gdppc>13600</gdppc>

        <neighbor name="Costa Rica" direction="W"/>

        <neighbor name="Colombia" direction="E"/>

    </country>

</data>

1、解析xml文件

>>> os.getcwd()

'D:\\workspace\\testpython'

>>> import xml.etree.ElementTree as ET

>>> tree = ET.parse('test.xml')

>>> root = tree.getroot()

>>> print root

<Element 'data' at 0x1d2a8b0>

>>> print tree

<xml.etree.ElementTree.ElementTree object at 0x01D2A9D0>

>>> root.tag

'data'

>>> root.attrib

{}

>>> #遍历子节点

>>> for child in root:

    print child.tag,child.attrib

country {'name': 'Liechtenstein'}

country {'name': 'Singapore'}

country {'name': 'Panama'}

>>> root[0].text

'\n        '

>>> root[0][1].text

''

>>> root[1][3].text

>>> root[1][2].text

''

2、查找元素：root.iter()迭代，element.findall()，element.find()，element.get()，element.text

>>> #查询元素

>>> for neighbor in root.iter('neighbor'):

    print neighbor.attrib

{'direction': 'E', 'name': 'Austria'}

{'direction': 'W', 'name': 'Switzerland'}

{'direction': 'N', 'name': 'Malaysia'}

{'direction': 'W', 'name': 'Costa Rica'}

{'direction': 'E', 'name': 'Colombia'}

>>> root.iter('neighbor')

<generator object iter at 0x01D3CF30>

>>> root.findall('country')

[<Element 'country' at 0x1d2aa90>, <Element 'country' at 0x1d2ad30>, <Element 'country' at 0x1d2af10>]

>>> for country in root.findall('country'): #element.findall()查询当前元素的子元素

    rank=country.find('rank').text  #element.find()查询指定标签的第一个子元素，element.text获取元素的内容

    name=country.get('name')  #element.get()获取元素的属性值

    print name,rank

Liechtenstein 1

Singapore 4

Panama 68

3、修改xml文件：element.set()修改属性，element.append()增加子元素，element.remove()删除元素

修改元素属性：

>>> for rank in root.iter('rank'):

    new_rank = int(rank.text) + 1

    rank.text=str(new_rank)

    rank.set('updated','yes')

>>> tree.write('test.xml')

aaarticlea/png;base64,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*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*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" alt="" />

删除元素：

>>> for country in root.findall('country'):

    rank = int(country.find('rank').text)

    if rank>50:

        root.remove(country)

>>> tree.write('test.xml')

<?xml version="1.0"?>

<data>

    <country name="Liechtenstein">

        <rank updated="yes">2</rank>

        <year>2008</year>

        <gdppc>141100</gdppc>

        <neighbor name="Austria" direction="E"/>

        <neighbor name="Switzerland" direction="W"/>

    </country>

    <country name="Singapore">

        <rank updated="yes">5</rank>

        <year>2011</year>

        <gdppc>59900</gdppc>

        <neighbor name="Malaysia" direction="N"/>

    </country>

</data>

4、构建xml文件：SubElement()

>>> a = ET.Element('a')

>>> b = ET.SubElement(a, 'b')

>>> c = ET.SubElement(a, 'c')

>>> d = ET.SubElement(c, 'd')

>>> ET.dump(a)

<a><b /><c><d /></c></a>

>>>

5、用命名空间来解析xml文档

If the XML input has namespaces, tags and attributes with prefixes in the form prefix:sometag get expanded to {uri}sometag where the prefix is replaced by the full URI. Also, if there is a default namespace, that full URI gets prepended to all of the non-prefixed tags.

Here is an XML example that incorporates two namespaces, one with the prefix “fictional” and the other serving as the default namespace:

<?xml version="1.0"?>

<actors xmlns:fictional="http://characters.example.com"

        xmlns="http://people.example.com">

    <actor>

        <name>John Cleese</name>

        <fictional:character>Lancelot</fictional:character>

        <fictional:character>Archie Leach</fictional:character>

    </actor>

    <actor>

        <name>Eric Idle</name>

        <fictional:character>Sir Robin</fictional:character>

        <fictional:character>Gunther</fictional:character>

        <fictional:character>Commander Clement</fictional:character>

    </actor>

</actors>

>>> import xml.etree.ElementTree as ET

>>> tree=ET.parse('namespace.xml')

>>> root=tree.getroot()

>>> for actor in root.findall('real_person:actor', ns):

    name = actor.find('real_person:name', ns)

    print name.text

    for char in actor.findall('role:character', ns):

        print ' |-->', char.text

John Cleese

 |--> Lancelot

 |--> Archie Leach

Eric Idle

 |--> Sir Robin

 |--> Gunther

 |--> Commander Clement

>>>

定位、编辑、保存元素属性：

>>> import xml.etree.ElementTree as ET

>>> tree=ET.parse('spring-subtract.xml')

>>> root=tree.getroot()

>>> print root

<Element '{http://www.springframework.org/schema/beans}beans' at 0x1d423f0>

>>> print root.getchildren()

[<Element '{http://www.springframework.org/schema/beans}bean' at 0x1d422f0>, <Element '{http://www.springframework.org/schema/beans}bean' at 0x1d422d0>, <Element '{http://www.springframework.org/schema/beans}bean' at 0x1d425d0>]

>>> print root.getchildren()[2]

<Element '{http://www.springframework.org/schema/beans}bean' at 0x1d425d0>

>>> print root.getchildren()[2].getchildren()

[<Element '{http://www.springframework.org/schema/beans}property' at 0x1d42610>, <Element '{http://www.springframework.org/schema/beans}property' at 0x1d42670>]

>>> print root.getchildren()[2].getchildren()[1].attrib

{'name': 'cronExpression', 'value': ''}

>>> print root.getchildren()[2].getchildren()[1].attrib['value']

0000

>>> #编辑属性值

>>> root.getchildren()[2].getchildren()[1].set('value','')

>>> tree.write('spring-subtract.xml')  #保存文件

>>> print root.getchildren()[2].getchildren()[1].attrib

{'name': 'cronExpression', 'value': ''}

>>>

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